Online marketplace for automatically extracted data

ABSTRACT

A system for automatically locating and data-typing information originating from many Web pages, and then collecting that information in a database. The database is then made available via an online data marketplace which allows users from different organizations to buy and sell related data, associated advertisements, and access to the communities of end-users who may also view advertisements and make purchases.

BACKGROUND OF THE INVENTION

The World Wide Web contains billions of pages of freely availableinformation, such as airplane arrival times, baseball statistics, andproduct descriptions. However, much of that information is embedded inrunning prose intended for reading by humans. A human is best equipped,for example, for locating the information on a Web page, giving it adata type (whether “1938” is a calendar year, the price of a product, oran airline flight number), and relating it to other data (“this picturelocated here depicts that product located there”). This manual processis time-intensive and error-prone.

There are current two ways to extract data automatically from a Webpage, a process which is called “Web scraping”. First, every Web pagecontains hidden mark-ups for formatting, such as boldface and italics.Theoretically, these mark-ups can help a computer algorithm locateinformation on a page. Unfortunately, every Web site has a differentlook and feel, so each Web page needs its own custom algorithm. Writinga custom algorithm is time-intensive, but possible on a small scale,such as a price comparison website which gathers product informationfrom a dozen sources. But there is no efficient way to scale thisapproach up to thousands or millions of Web sites, which would requirethousands or millions of custom algorithms to be written.

The second method requires the owner and developer of each Web site toadd hidden mark-ups that specifically designate information and its datatype. The preferred technology for this is XML. Unfortunately, nearlyall Web sites are not built this way, and there are no standardizedterms for XML usage. It is a little like saying that if only everyonewould speak Esperanto, there would be no translation problems. This istrue in theory, but hopelessly impractical.

Once data has been collected, there are no good mechanisms fordisseminating it. Every Web site that publishes information standsalone. Each publisher writes its own content, sells its own ads, andmanages its own online community. Web publishers such as Amazon.com thatinclude others' book reviews, and such as The Boston Globe that includeothers' newswire stories, require their partner's active participationto integrate their databases together. This function is also quitedifficult to scale up to millions of potential partners and thetrillions of possible bilateral partnerships between those potentialpartners. The matter becomes even more complicated when advertisements,which can come from thousands of sources, need to be associated withdata and presented to the end-users who read the publisher's Web site.Finally, there is currently no easy way for the online communities ofvarious Web sites to profit from each other's knowledge, forming a“meta-community” which could, for example, automatically share moviereviews and ratings across thousands of movie fan Web communities.

SUMMARY OF THE INVENTION

There exists a need for a low-cost, highly-automated method for“scraping” information from the World Wide Web, forming partnerships totrade this data, and presenting it to readers alongside advertisementsfrom any source.

Briefly, the present invention provides a system for automaticallylocating and data-typing information from thousands of Web pages, andthen collecting that information in a central database. The database isthen made available via an online data marketplace which allows usersfrom thousands of different organizations to buy and sell related data,associated advertisements, and access to the communities of end-userswho may also view advertisements and make purchases. These innovationsmay be used together or separately.

Web pages contain running text, in English or some other language, whichis designed to be read by humans. Thus, extracting the data embedded inthat text, data type information and context would seem to be adifficult problem for a computer algorithm. However, some automation ispossible because many Web pages can be grouped as similar in appearanceand format. For example, every book description Web page on Amazon.comlooks the same as every other. If a human locates and types informationon one Amazon.com Web page, then a computer may be able to locate andtype data on all of the millions of similar-looking Web pages onAmazon.com. The challenges are then

(a) what is the best user interface for a human to identify for acomputer which element of a Web page contains the desired information,and the information's data type and relation to other data?

(b) What is the most flexible way to store and communicate thisknowledge?

(c) How can a computer generalize from one Web page to extractinginformation from millions of similar looking Web pages, even if they donot a match precisely?

(d) In what ways can the need for human involvement be minimized, andprobable errors be identified automatically for review?

(e) What is the best user interface to report errors to a human and havethem step in to fix the situation?

(f) What modifications are required to target specific vertical markets?

These problems are solved with a method according to a preferredembodiment of the invention in the following way:

(a) Using the mouse and a Web browser, a human interacts with a program(such as running on an application server) and highlights information ona page and right-clicks to bring up a dynamically-generated menu topermit the user to select the data type.

(b) Information as to data type is then stored directly into a copy ofthe Web page by the server.

(c) Web pages typically include not only prose but also text formattingmarkup tags (such as <b> that cause text to be displayed in boldface).The server can match an element on a template to an element on a sourceWeb page to another by defining a set of “contextual clues” thatcharacterize an element's location in the context of its Web page. Thenthe server makes a map of these features, which can be used later tonavigate around the Web page.

(d) Natural language algorithms using word frequency statistics can alsobe used to characterize extracted data, and thus provide suggestions tothe human user for rapid information location and data typing. Theseword frequency statistics can also be used to evaluate the result ofautomated extraction for likely correctness.

(e) An interface similar to the debuggers used for computer programminglanguages can be used to report the results of data typing.

(f) For specific vertical markets, the data may be extracted as lines oftext that require further processing (e.g. extracting the time-of-dayfrom a text string such as “Hours of Operation: Monday to Friday, 8 amto 5 pm, except Holidays”). Specially written parsing algorithms can beused, because the vocabulary in such a domain is limited (e.g., todetermining time-of-day ranges).

Once data has been collected, a further mechanism can be employed sothat the data can be freely traded and published. A database suitablefor storing information scraped from Web sites, in one embodiment,differs from standard databases in several ways:

(a) the Web page that is the source for the data may change regularly,requiring a moderator to configure an information flow rather than storestatic data 1006

(b) data may be sourced from numerous Web pages, which should beassembled 506;

(c) users of the database, e.g., a publisher of a Web site, may have acommunity that will contribute numeric ratings, and prose commentary andthe like to the data 1004; managing this centrally so that the opinionsof differing communities can be shared is another desirable feature1006;

(d) publishers of Web information may often want to associateadvertisements with the data, in as targeted a way as possible, toachieve the highest level of accuracy. Targeting advertisements towardsinformation scraped from Web sites may require special algorithms 1010;and finally

(e) Web scraping algorithms may occasionally gather the wronginformation, requiring a technique to automatically identify and rejectthis information 507.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features, and advantages of the invention willbe apparent from the following more particular description of preferredembodiments of the invention, as illustrated in the accompanyingdrawings in which like reference characters refer to the same partsthroughout the different views.

The foregoing will be apparent from the following more particulardescription of example embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingembodiments of the present invention.

FIG. 1 is a high level diagram of a data processing environment in whichthe invention may be implemented.

FIG. 2 illustrates a data schema that defines data typing and datainter-Relationships.

FIG. 3 a is a copy of a Web page with data.

FIG. 3 b is a sequence of steps for setting up a Web page to be“scraped”.

FIG. 3 c is a template: a copy of Web page with data marked up.

FIG. 4 illustrates a Web page that has been set up with marks.

FIG. 5 a illustrates a sequence of steps for “Web scraping”: gatheringdata from Web Sites.

FIG. 5 b is a visual representation of Web scraping in action.

FIG. 6 illustrates example contextual clues and navigational steps toprovide clues for navigating through a Web page.

FIG. 7 is a conceptual diagram illustrating how the processes matchelements on a template to elements on a source Web page.

FIG. 8 illlustrates a sequence of steps for how elements are located onthe source Web page.

FIG. 9 illustrates an example page where the locations of elementscontaining the desired information have been identified.

FIG. 10 illustrates an online marketplace for information scraped fromWeb sites and a “meta-community”.

FIG. 11 is an example of a personalized Web page of activities embeddedin a Web publisher's own Web site.

DETAILED DESCRIPTION OF THE INVENTION

A description of preferred embodiments of the invention follows.

Overview

This preferred embodiment is in the arts & entertainment industry. Artsand entertainment events are typically listed across thousands of Websites. Gathering, trading, and publishing this information is ofsubstantial value to Web Publishers 111, Advertisers 108, and the OnlineCommunity 112 for each of the published Web sites.

FIG. 1 shows an overview of a data processing environment in which theinvention may be used. First, the Set Up Expert 100 characterizes thedata domain of the data to be gathered from the Web, using a Data Schema113. For example, if the data domain is automobiles then the Data Schema113 would specify that cars have a make, model, and year of manufacture.Having built the Data Schema 113, the Set Up Expert 100 uses the Set UpSystem 101 to browse to a Web page and mark the location of information,creating a template. This may be repeated across thousands of Web sites,but one template will usually suffice for a single page, and an entiregroup of Web pages that have similar look and feel, for all timethroughout their changes and updates. A Web server then uses thisconfiguration for Daily Web “Scraping” 103, a term which means readingsource Web pages and extracting information using the appropriatetemplate.

The extracted information is stored in a Database 104. This Database 104feeds data into a Publishing System 110 which can be used by each ofseveral Web Publishers 111 to provide information to their own OnlineCommunity 112, of which there is one for every Web publisher. TheDatabase 104 is itself fed by an Online Data Market 105, which allowsBuyers 106 and Sellers 107 to freely trade primary and auxiliaryinformation relating to data flows that come from Web site, effectivelycreating a meta-community from potentially thousands of different onlinecommunities. An Ad System 109 allows Advertisers 108 to registeradvertisements with the system, which are matched with information inthe Online Data Market 105. This matching presents advertisements to theOnline Community 112 that are relevant to their interests and thus morelikely to stimulate Advertisers 108 to spend money.

Setting Up a Web Page to be “Scraped”

Because the data domain is Arts and Entertainment event listings, theSet Up Expert 100 characterizes this data domain by creating a DataSchema 113. A typical way to do this would be using the databaselanguage SQL, or as class definitions in Java. FIG. 2 shows an exampleData Schema 113, the Data Schema for Arts & Entertainment Event Listings200, which defines for each data class, its data type, and its datainter-relationships. For example, each Activity 202 has a Venue 201 andan Organizer 203. Every Venue 201 has an address. Error-checkinginformation is included in the schema. For example, addresses should notbe more than 50 words in length. This error-checking information can bemanually set up or computed using statistics from known examples.

FIGS. 3 a, 3 b and 3 c illustrate the manual set up that is required togather information from a Web site. First the Set Up Expert 100identifies target Web sites that are relevant to the data domain. Inthis preferred embodiment, the data domain is Arts and Entertainmentevents, so the Set Up Expert 100 would target museum, concert hall,student club, festival organizer, and similar Web sites. Such sites maycontain event calendars with relevant information embedded within. Oncea few target Web sites have been identified, a statistical algorithm canidentify others on the Internet through word-frequency and word-locationmatching. The end result is a group of target Web sites from whichinformation can be drawn. For example, in New England, there are 3,000Web sites that list activities and events. These Web sites, which changeday-to-day, list 100,000 New England activities and events each month.

Each Web site can have dozens, thousands, or potentially millions of Webpages. Each Web page with a unique look and feel requires a template tobe manually set up. However, most Web pages belong to a group ofsimilar-looking Web pages. A group like this requires only onerepresentative Web page to be manually set up as a template. In thisexample, the Set Up Expert 100 identifies the Bayside Expo Center as amajor venue for conferences in the Boston, Mass. area. The Bayside ExpoCenter has a website at a well known .com address. One Web page on thatwebsite is a calendar of activities happening at the Bayside ExpoCenter.

In step 301, The Set Up Expert 100 directs the Set Up System 101 to makea copy of the calendar of events of the Bayside Expo Center, resultingin a Copy of Web Page With Data 300. The Copy of Web Page With Data 300is simply a copy of the Hyper Text Markup Language (HTML) of theoriginal Web page.

In this example, The Copy of Web Page With Data 300 contains informationabout the event, including its name, “The World of Wheels” 319, its timespan, “January 6-January 8” 320, and its organizer, “Championship AutoShows” 321. We also know that the event takes place at the Venue forthis website, The Bayside Expo Center. The Set Up Expert 100 wants toteach the system how to automatically scrape this information from thepage and all other Web pages in the group of similar-looking pages,which comprise the entire calendar of the Bayside Expo Center.

In step 301, The Copy of Web Page With Data 300 is displayed in a Webbrowser on which is running a Java applet. As shown in FIG. 3 a, Set UpExpert 100 uses the mouse to highlight items on the page. First, theuser assigns a type to the entire page, by highlight the “entire page”element 310 at the top of the page and right-clicking with the mouse. Adynamically generated drop-down menu 312 appears listing the data typesin the Schema 200. The user selects Venue 201 from the list, becausethis Web site is owned by The Bayside Expo Center, which is a venue.Then the user highlights the entire Activity 314, and right-clicks withthe mouse.

This time the drop-down menu 312, which is dynamically generated, makessome guesses about the data type that is most appropriate for theelement that was just highlighted. Since the page itself is a Venue 201,and the Data Schema for Arts & Entertainment Event Listings 200 saysthat every Activity 202 has a Venue 201, one of the elements of thedrop-down menu will be Activity 316, which the user selects, In this waythe dynamically generated drop-down menu 312 is making it simpler andfaster for the user to identify data types, by automatically suggestingwhat seems most relevant. Word frequency statistics can be used in thecreation of such suggestions. For example, if the user highlights a 10digit number with dashes that is most likely a phone number, thedrop-down menu would place “Phone Number” at the top of the dynamicallygenerated drop-down menu.

In step 302, the Set Up Expert 100 highlights “World of Wheels” 319.Then in step 303, the user right-clicks, again bringing up a dynamicdrop-down menu. According to the Data Schema for Arts and EntertainmentEvent Listings 200, each Activity 202 is associated with a name, hoursof operation, organizers, and other data. These possibilities are listedin the dynamically created drop-down menu, and the user selects “name”322. Then in step 304, the computer then places special annotations intothe Copy of Web Page With Data 300 to record these facts.

Similarly, in step 305, the Set Up Expert 100 associates “January6-January 8” 320 as the time span for the event, and “Championship AutoShows” 321 as an organizer 326 (see FIG. 3 c). This information isdisplayed in The Copy of Web Page With All Data Marked Up 207. When theuser is finished, in step 306, the Set Up System 101 stores the Copy ofWeb Page With Data 300 as a template for future use.

This Template Contains:

-   The original Web page's HTML in full-   Annotations showing:    -   The location of the element on the Web page that contains the        desired information    -   The data type of the information    -   The relation between this information and other data on this        page or elsewhere

The drop-down menu 312 includes the item “anchor”, which allows the userto indicate that the highlighted text on the Web page should neverchange. This annotation would also be stored as an embedded tag in TheCopy of Web Page With Data 300.

The drop-down menu 312 also includes the item “link”, which allows theuser to indicate that a link on the Web page is important. Any link theuser clicks on is automatically read as important, as well. Theintention is that during the Web scraping phase, if a Web page beingread contains a link, the Web page being linked to will also be scraped,using the appropriate template.

Finally, the user may also indicate that some text region of the Webpage is a list of blocks, and each block is treated as if it were aseparate Web page with its own template. For example, the calendar ofevents at the Bayside Expo Center is one big list of identicallyformatted event summaries, each of which links through to an identicallyformatted event details page. A template from one of the event detailpages will thus suffice to read information from the rest.

FIG. 4 shows the resulting embedded markups in the Template: A Copy ofWeb Page With Data Marked Up, in HTML Format 400. The specialannotations created by the Set Up System 101 are highlighted. There isno difference between this and the Template: A Copy of Web Page WithData Marked Up 307. It is the same HTML page displayed differently—firstin a Web browser and then in raw text format.

“Web Scraping”: Gathering Data from Web Sites

Once the Set Up Expert 100 has marked up several or possibly thousandsof Web sites, FIG. 5 a illustrates how data is gathered.

Web scraping is run as a batch job on Daily Web Scraping 103 that can berepeated monthly, daily, hourly, or more frequently. Different datadomains will tend to change more or less frequently, requiring more orless frequent Web scraping. An event calendar, for example, may beupdated daily, but a Web page with stock market fluctuations may changeevery minute.

The starting point in Step 500 is to gather all the templates from theDatabase 104 that are associated with a permanent URL. A permanent URL,for example, would be the home page of the Bayside Expo Center eventscalendar, which resides at a known URL and will never be locatedelsewhere. Other templates, those without a permanent URL, are accessedthrough the user-identified links on Web pages already being processed.

Then in Step 501, all the templates with permanent URLs are sent forprocessing, Step 502. The first step in processing, Step 503, is to usethe URL to fetch a source Web page in real-time from the Internet. Thissource page is fully up-to-date with whatever information the Webpublisher owning that Web page has got currently posted on theirwebsite. Then the server applies the template to the source Web page,matching the elements of the template to the elements of the Web page,and extracting the desired information, its data type, and itsinter-relationship to other data. Exactly how this is done is describedin the next section. For example, the Bayside Expo Center events pagewould be loaded and compared with the appropriate template. The big listof events would be discovered.

Then in Step 504, if the source Web page contains any lists, those listsare now processed. For example, a list 530 was found on the eventcalendar page of the Bayside Expo Center in FIG. 5 b. A list is a seriesof blocks 509, each on one line, each of which is processed against atemplate just as Web pages are processed against templates in 503. Inthis case, the Bayside Expo Center has a series of brief eventdescriptions which link into pages with detailed descriptions, such asthe “World of Wheels” page shown in 300. Each of these brief eventdescriptions is scraped for information.

The last step in processing a template against a URL is Step 505, tohandle any links that were discovered in the list. Each of the blocks509 on the Bayside Expo

Center event calendar list has a link, as noted in the previousparagraph. Each link is associated with the template for scraping theWeb page that is linked to. As one example, there is a link 550 to the“Boston Home Show” event page. The Web Scraper 103 proceeds to load thepage linked to, the “World of Wheels” page. The template 307 derivedfrom the “World of Wheels” event page 307 is compared against the“Boston Home Show” event page, a comparison is made, and data isextracted 560. The extracted data is as then stored with their datatypes (Venue 201, Activity 202, etc.).

To summarize, the entire Web site can be read when the Set Up Expert 100has only set up two pages, the Bayside Expo Center events calendar pageand the World of Wheels event details page. From this rapid manuallabor, the Daily Web Scraping 103 can now proceed automatically and readevery events page on the entire website, both that day and every day inthe future.

Finally, after all the pages and the pages they link to have been readand processed, in Step 506, the data that has been gathered ispost-processed to connect data together, resolve conflicts, and reportpossible errors. Then in Step 507, using the Set Up System 101, the SetUp Expert 100 corrects any remaining errors and resolves any remainingconflicts. The resulting data may resemble A Visual Representation ofWeb Scraping in Action 508.

How Information is Located on the Web Page

Given a template, such as Template: A Copy of Web Page With Data Marked

Up, 307, and a page to read, such as the “Boston Home Show” page on theBayside Expo Center (see FIG. 9), how can the computer locate anddata-type fields such as Title: “Boston Home Show”, Hours: “January13-January 15”, Organizer: “Pat Hoey Productions”, as shown in A VisualRepresentation of Web Scraping in Action 508? Since the data-type isembedded in the template 307, the problem can be distilled down tolocation. Once we have matched every element in the template indicatingdesired information with the corresponding element in the source Webpage, the data typing and data inter-relationships are simply given fromthe template's element.

FIG. 5 illustrates the contextual clues needed to locate information ona Web page. In Many Locations Exist on the Source Web page 600, thereare nine locations identified, all HTML tags, white space, or runningtext such as “Boston Home Show”. The trick is to identify which locationon the source Web page (“Boston Home Show”) matches up with thehighlighted location on the template (“World of Wheels”).

Every location has contextual clues, such as which tag surrounds orprecedes it, as shown in Contextual Clues Helping Specify a Location601. In addition, two adjacent locations will have a relationship toeach other, as illustrated in Adjacency Relationships In-BetweenNeighboring Elements 602.This information helps identify matches betweenelements on the template and elements on the source Web page, eventhough we cannot rely on the source Web pages associated with a templateto have identical formats today and for all time. The text is likely tovary significantly, and the tags and general structure of the source Webpage may change slightly too.

FIG. 7 shows the approach to matching up the elements of the templatewith the corresponding elements of the source Web page. The algorithmfor matching locations between a template and a source Web page beginswith the matches that are highest confidence, which become “anchors”.Those anchors give further contextual clues to place down otherlocations in-between known anchors.

FIG. 8 is a formal description of the algorithm for locating informationon a source Web page using a template. In step 800, a range is definedbetween the start and end points of the two Web pages being matched. Instep 801, every known template element F is examined, and every possiblelocation of that element E on the source Web page is examined, to findall the E-and-F match ups in which we have very high confidence. Asshown in step 802, this is done using the above described contextualclues and adjacency relationships as a scoring system and using aweighted least squares algorithm. In step 803, if no high-confidencematches are found, the algorithm recursively backtracks and may signal ahuman for assistance.

In step 804, we choose the highest confidence match is chosen and instep 805 this becomes an anchor point, splitting the START-to-END regioninto two regions: START-to-ANCHOR, and ANCHOR-to-END. This transformsthe problem into smaller regions where all of the neighboring locationsto ANCHOR can now be located by returning to step 801.

Although this would seem to be a slow algorithm, since it involves allcombinations of E and F, in practice there are typically several uniqueor very high confidence matches which can be located immediately,dividing the problem into small fragments. One complexity is that sincethings may be added or deleted from a Web page over time, a separateweighted least squares algorithm evaluates the possibility that one ofthe elements of the template simply does not exist in the source Webpage, or exists but something additional has been added.

Online Market for Data Scraped from Web Sites

Historically, online marketplaces have been created for buying andselling antiques or trading stock over the Web. However, trading thedata scraped from Web sites presents new features. Referring to FIG. 10,

-   Web Publishers 1001 act as brokers for buying and selling    information for their respective Online Communities 1002-   Not only are Web Publishers 1002 charged monetarily for buying and    rewarded monetarily for selling; their Online Communities 1002 may    bear costs or reap rewards as well. How best to managing these flows    is an issue.-   Information generated by Online Communities 1002 should be policed    for accidental or malicious error-   The information that is to be traded is of a form never traded    before:    -   Event experts who sell reviews, photographs    -   Communities who share their ratings (each community's ratings        can be weighted when combined)    -   Moderators who choose a stream of events, like a DJ chooses        which music to play    -   Access to advertisers and access to communities    -   Event experts who use category tags to label an event for easy        reference    -   Data scraped from the Web is not static; it is a flow that is        frequently changing-   Finally, Advertisements can be targeted to differing communities    based on their differing statistics, increasing the effectiveness of    ads and therefore how much advertisers will pay.

What is happening is similar to podcasting. Audio broadcasts havetraditionally been expensive and complex to produce, and were dominatedby large corporations through radio stations. The Internet made itpossible for hobbyists to inexpensively produce their own audio shows,leading to a boom in creativity and content. In a similar way, althoughonline communities have existed for over a decade, for the first time,through the Online Data Market 1000, an entire community can acttogether to “broadcast” information to other communities. Onlinecommunities become lightweight and inexpensively created and managed.This paradigm explicitly includes a commercial buy and sell model,fostering incentives and creating one huge meta-community for any datadomain.

In previous sections of this description of a preferred embodiment, aregular daily scraping of thousands of arts & entertainment Web siteshas been set up, creating an ever-changing data flow of arts &entertainment activity listings.

Now, in Step 1005, a Web Publisher 1001 configure this stream ofactivities, choosing which portion of the whole will appear on his orher Web site for his or her Online Community 1002. The first way thiscan be done is through performing a query to the Database 104 and savingthat under a name. This query is then optimized so that updates areselected as new information is added to the Database 104 by the DailyWeb Scraping 103. This query may be based on keywords, or on categorytags. A category tag is text word such as “Over-18”,“Handicapped-Access”, or “Free” that can be applied to an eventexplicitly in an attempt to categorize it. A statistical matchingalgorithm is used to automatically apply category tags based on the textof a source Web page, starting from a seed of user-applied tags.

In Step 1005, Web Publisher 1001 has now configured a personalized Webpage on the Publishing System 110 which can be accessed from his or herown Web site by link or by including it as a frame or table inside oneof the Web Publisher's 1005 own Web pages. FIG. 11 shows an example ofthis, where the activity listings from Visual Representation of WebScraping in Action 508 have been inserted into a Web Publisher's 1001Web page. This personalized Web page will fill in automatically withactivity data. This stream of information can run freely from thedatabase to the online community, or each event can be moderatedindividually for approval before being presented to the onlinecommunity.

Then, in Step 1004, the Online Community 1002 adds content such asreviews, photographs, interviews, and ratings. This content may be freeor it may be compensated for by the Web Publisher 1005.

Then, in Step 1006, the Web Publisher 1001 configures rules for how thecontent created in Step 1004 by the Online Community 1002 is to be sold,if at all. The community's reviews in plain text, and photographs withcaptions can be bought and sold.

The key problem of selling content created by a community is that theoverall quality of volunteers is usually amateurish and not very good.However, in. Step 1007, the Online Data Market 1000 can help the WebPublisher 1001 moderate the content and separate the good from the badby assigning a utility score to the content that members of the OnlineCommunity 1002 are contributing. Based on these utility scores, the WebPublisher 1001 can approve content for sale through the Online DataMarket 1000, or manually intervene to remove accidentally or maliciouslyerroneous content.

In Step 1007, different types of content require different utilityscoring algorithms. The quality of the submission can be automaticallyjudged based on (a) statistics involving the words in the plain text andphotograph captions; (b) how often a Web visitor clicks on the content;(c) how long a Web visitor spends looking at the content; and (d)explicit ratings given by Web visitors. Some users may be trusted andhave immediate permission to sell information into the Online DataMarket 105 on behalf of the online community.

Then in Step 1008, a different Web Publisher 1003 wants to drawinformation from the Online Data Market 1000 for its own Web Community1004. This may be a selling—the Web Publisher 1003 may charge to publishany listing. Or, the data may be valuable enough that the Web Publisher1003 is buying it from Web Publisher 1001. Web Publisher 1003 configuresthe system to determine which communities information will be drawnfrom, what prices are reasonable to pay, and whether content will besparse or deeply filled in. Web Publisher 1003 can also outsource theentire moderation of the event stream through the Online Data Market1000. This would be similar to one DJ selling a playlist of music toanother DJ every day.

Based on demand and that configuration, in Step 1009 the Online DataMarket 1000 determines the appropriate prices and handles the transitionof money. In this case, instead of trading purely for money, Webpublishers 1001, 1003 accrue “points”, similar to how airlines use “airmiles”. Although these points can be redeemed for cash, they can also beused to provide services for an online community. For example, ifBugaboo Creek Steakhouse has an advertisement with a coupon good for $15off a meal, the publisher 1001 may spend points to purchase 250 of thesecoupons and present them to his or her online community. Creatingincentives for the community to provide content, the Web publisher cantake a cut and then finance the original incentives by sales into theOnline Data Market 1000.

Additionally, in Step 1010, algorithms can select and suggest contentfor the end-user based on their explicit tastes (ratings) and theirimplicit tastes as demonstrated by their browsing history and thecommunity they have chosen to join. These algorithms can select for themost relevant content and serve to sort lists of events with the onesmost likely to be of interest on the top. Additionally, advertisementscan be selected by an algorithm that matches ads with the end-users mostlikely to click on them.

Finally, in Step 1011, Ratings that are contributed by the OnlineCommunity 1006 need to be combined with the ratings from othercommunities. This is done using a weighted scoring system that isbalanced from what the end-users tastes seem to be, as recorded by thehistory of browsing events.

In addition to this, a Publishing System 110 allows any Web publisher tomanage the online community, and annotate events and activities withadditional expert content, such as reviews, ratings, and photography. AnAdvertising System 109 allows advertisers to post their own ads andconfigure the system with hints about which events and category tagswould be most relevant to the ad. This information is then used whendetermining which ads to show to end-users.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. A method for extracting information from a collection of sourcepages, comprising: identify a schema for a domain that defines datarelationships and data types expected for source pages in a givendomain; for a specific source page, creating a template associated withthe source page; allowing a user to identify a region using the sourcepage; and for the identified region, using user input to determine adata type and inter-relationship to other data.
 2. A method as in claim1 further comprising: accepting user input identifying the highlightedregion; examining the schema; and displaying a list of likely datatypes.
 3. A method as in claim 1 additionally comprising; for aplurality of origin pages in the domain, matching the template to thesource page to identify data elements in the source page that match theannotated data in the template; and storing data elements in a databaseassociated with the domain, based on the schema.